Visual Detection of Objects by Mobile Agents using CBVIR Techniques of Low Complexity

To monitor objects of interest, such as wildlife and people, image-capturing devices are used to collect a large number of images with and without objects of interest. As we are recording valuable information about the behavior and activity of objects, the quality of images containing objects of interest should be better than that of images without objects of interest, even if the former exhibits more severe distortion than the latter. However, according to current methods, quality assessments produce the opposite results. In this study, we propose an end-to-end model, named DETR-IQA (detection transformer image quality assessment), which extends the capability to perform object detection and blind image quality assessment (IQA) simultaneously by adding IQA heads comprising simple multi-layer perceptrons at the top of the DETRs (detection transformers) decoder. Using IQA heads, DETR-IQA carried out blind IQAs based on the weighted fusion of the distortion degree of the region of objects of interest and the other regions of the image; the predicted quality score of images containing objects of interest was generally greater than that of images without objects of interest. Currently, the subjective quality score of all public datasets is in accordance with the distortion of images and does not consider objects of interest. We manually extracted the images in which the five predefined classes of objects were the main contents of the largest authentic distortion dataset, KonIQ-10k, which was used as the experimental dataset. The experimental results show that with slight degradation in object detection performance and simple IQA heads, the values of PLCC and SRCC were 0.785 and 0.727, respectively, and exceeded those of some deep learning-based IQA models that are specially designed for only performing IQA. With the negligible increase in the computation and complexity of object detection and without a decrease in inference speeds, DETR-IQA can perform object detection and IQA via multi-tasking and substantially reduce the workload.

Abstract-With the explosion in demand for visual information retrieval in soccer videos, many Content-Based Video Retrieval (CBVR) models were born. However these current CBVR models still remain some shortcomings such as inflexible retrieval frameworks because they are majorly based on a specific training data set and a specific language. In this paper we propose a framework which contains flexible and easy-to-use queries based on visual and audio data indexing in soccer videos. Our framework understands a soccer event which is constructed by the following visual components: shot with shot types, interest objects, and audio components. In which, an event can be retrieved from coarse to fine by finding relevant shot-sequence, after that some specific visual and audio objects are related to events. Our experiment on AFF-Suzuki Cup Tournament 2009 video data set showed better retrieval results with events such as goal, foul, shoot, corner kick, off side, red (yellow) card regarding precision and recall than the traditional systems only based on visual or audio queries.

One of the important features in content based image retrieval is color feature. The color feature is the most widely used visual features. Extracting feature image depends on the problem to identify the region or object of interest that is complex in content. This paper presents a methodology to recognize certain freshwater images using region of interest and color feature. In this work, we have considered 7 varieties of freshwater fish, Gourami, Mas/Common carper, Mas Orange, Mas Kancra, Mujair/Java Tilapia, Nila/Nile Tilapia, and Patin. Each variety consists of 20 images. We deployed Color Moment Feature after Region of Interest process to extract the feature. Euclid is used for recognition. Considering only a feature, the classification accuracy of 89% is obtained using color moment. The research technique shows promise for eventually being able to do so, and for the future will help to get important information from the image.

Content-based image retrieval (CBIR) is one of the most important applications of computer vision. In recent years, there have been many important advances in the development of CBIR systems, especially Convolutional Neural Networks (CNNs) and other deep-learning techniques. On the other hand, current CNN-based CBIR systems suffer from high computational complexity of CNNs. This problem becomes more severe as mobile applications become more and more popular. The current practice is to deploy the entire CBIR systems on the server side while the client side only serves as an image provider. This architecture can increase the computational burden on the server side, which needs to process thousands of requests per second. Moreover, sending images have the potential of personal information leakage. As the need of mobile search expands, concerns about privacy are growing. In this article, we propose a fast image search framework, named DeepSearch, which makes complex image search based on CNNs feasible on mobile phones. To implement the huge computation of CNN models, we present a tensor Block Term Decomposition (BTD) approach as well as a nonlinear response reconstruction method to accelerate the CNNs involving in object detection and feature extraction. The extensive experiments on the ImageNet dataset and Alibaba Large-scale Image Search Challenge dataset show that the proposed accelerating approach BTD can significantly speed up the CNN models and further makes CNN-based image search practical on common smart phones.

Images play important role in telerobotic interface systems. A set of images taken by an image sensor equipped on a mobile robot is helpful for a remote user to recognize the conditions of the robot as well as its operating environments. We view such an image set as a large-scale image database, and apply Content-Based Image Retrieval (CBIR) to visualize the image database. We will propose a novel CBIR method that efficiently searches such relevant objects based not only on their features, but also on their locations with respect to the operating environment.

Contour or boundary descriptors may be used in content-based image retrieval to effectively identify appropriate images when image content consists primarily of a single object of interest. The registration of object contours for the purposes of comparison is complicated when the objects of interest are characterized by open contours and when reliable feature points for contour alignment are absent. We present an application that employs an iterative approach to the alignment of open contours for the purposes of image retrieval and demonstrate its success in identifying individual bottlenose dolphins from the profiles of their dorsal fins.KeywordsFeature PointActive ContourBottlenose DolphinSperm WhaleHumpback WhaleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Content based image retrieval systems (CBIR) are used to search images on the basis of their visual content in a huge image database. This approach uses a multi-clustering technique with a multi-searching process. In addition it integrate, Mean SIFT (Scale Invariant Feature Transform) descriptor as local feature and HSV(hue, saturation, value) histogram as global feature. Our proposition aims to a maximum separation of the execution to gain time and keep the performance of each descriptor. Local and global features are combined for more relevant results. Getting several views to relevant results to cover the subjectivity in displaying results. This article, detailed our proposed method with a comparison with the FIRE (Flexible Image Retrieval) Engine and LIRE (Lucene Image Retrieval). The results demonstrate the feasibility and relevance of our proposition.

PurposeContent‐based image retrieval (CBIR) technologies offer many advantages over purely text‐based image search. However, one of the drawbacks associated with CBIR is the increased computational cost arising from tasks such as image processing, feature extraction, image classification, and object detection and recognition. Consequently CBIR systems have suffered from a lack of scalability, which has greatly hampered their adoption for real‐world public and commercial image search. At the same time, paradigms for large‐scale heterogeneous distributed computing such as grid computing, cloud computing, and utility‐based computing are gaining traction as a way of providing more scalable and efficient solutions to large‐scale computing tasks.Design/methodology/approachThis paper presents an approach in which a large distributed processing grid has been used to apply a range of CBIR methods to a substantial number of images. By massively distributing the required computational task across thousands of grid nodes, very high through‐put has been achieved at relatively low overheads.FindingsThis has allowed one to analyse and index about 25 million high resolution images thus far, while using just two servers for storage and job submission. The CBIR system was developed by Imense Ltd and is based on automated analysis and recognition of image content using a semantic ontology. It features a range of image‐processing and analysis modules, including image segmentation, region classification, scene analysis, object detection, and face recognition methods.Originality/valueIn the case of content‐based image analysis, the primary performance criterion is the overall through‐put achieved by the system in terms of the number of images that can be processed over a given time frame, irrespective of the time taken to process any given image. As such, grid processing has great potential for massively parallel content‐based image retrieval and other tasks with similar performance requirements.

Segmentation aims at partitioning area in the image based on color, shape or textures. It is useful in many computer vision applications such as medical image analysis, object detection and recognition, and content-based image retrieval (CBIR). Currently, CBIR has been applied in an online clothing purchasing system. Here, the goal of the segmentation process is to segment the clothing parts from the background and the model skin. Practically, we want to detect the skin color and segment it from the image, and left the apparel parts in the image for matching processes. We propose an improved threshold-based segmentation technique by adding a new constraint to the previous method to limit the skin tone selection. The technique improved the segmentation results by correctly segment the apparels with the shade of purple, pale pink, and pinkish-yellow. Simulations on total 100 garment images showed that the proposed method increased the segmentation rate by 27%.

Social networking sites like Facebook and Flickr allows users to upload billions of images, famous e commerce web sites such as flipkart, amazon etc. are also provides number of product related images to user. Images which are present in social multimedia networking websites are accompanied by different tags, comments, annotations and other related information which tend to form an image rich information networks. Due to existence of different related information to images (tag, annotation etc.) retrieval of images from image rich information networks is very challenging and complex task. In this paper we have proposed an integrated approach (S-Cbir) to retrieve an images from image rich information networks. The proposed algorithm consists integration of two different techniques such as link based image retrieval and content based image retrieval systems. K-Simrank algorithm is first applied to find out the node similarity in image rich information networks and then CBIR technique is applied to find out relevant images. SIFT, CEDD, feature and texture extraction descriptors are used in CBIR technique. The search results shows that proposed algorithm refines the search results and also improves the accuracy of search.

VIMA has experienced an increasing demand for Content-based Image Retrieval (CBIR) systems since late 2004. In this paper, we report the search, filtering, and annotation systems that we have developed and deployed, and the user models of these systems. The objective of this paper is to provide to the researchers and developers in the area of image retrieval, guidelines for measuring the performance of their algorithms/systems, in a way that is consonant with the requirements of the users. We also enumerate technical challenges of building CBIR systems, and outline our solutions to tackle these challenges.

Localization and obstacle avoidance are important problems for indoor robots. Visual-based localization (VBL) is a promising self-localization approach that identifies a device's location in a 3D space by using cameras to see the device's surrounding scenes and objects. In this paper, we present a pictorial planar surface based 3D object localization framework. However, the image shaking on moving robot leads to localization accuracy reducing. In order to improve the localization accuracy on moving robot, the depth information from RGBD camera is involved to correct the pose calculation. Furthermore, in order to produce a more acceptable decision on obstacle avoidance, we also design an optimal path planning using RGBD camera based object detection. We have built an autonomous moving robot that can self-localize using its on-board camera and the PDPose (Picture Depth Pose) technology. The experiment study shows that our localization methods are practical, have a very good accuracy, and can be used for real time robot navigation. Moreover, compared with the traditional obstacle method, the optimal obstacle method produces better path planting result.

The use of is widespread in the biological domain, and has also been used extensively by armed forces around the world in order to make visual detection and classification of objects of military interest more difficult. The recent advent of ever more autonomous military agents raises the questions of whether can have a similar effect on autonomous agents as it has on human agents, and if so, what kind of will be effective against such adversaries. In previous works, we have shown that image classifiers based on deep neural networks can be confused by patterns generated by generative networks (GANs). Specifically, we trained a classifier to distinguish between two ship types, military and civilian. We then used a GAN to generate patterns that, when overlaid on parts of military vessels (frigates), made the classifier confuse the modified frigates with civilian vessels. We termed such patterns adversarial camouflage (AC) since these patterns effectively the frigates with respect to the classifier. The type of attack described in our previous work is a so-called white box attack. This term describes attacks that are devised given full knowledge of the classifier under attack. This is as opposed to black box attacks, which describe attacks on unknown classifiers. In our context, the ultimate goal is to design a GAN that is capable of black box attacks, in other words: a GAN that will generate AC that has effect across a wide range of neural network classifiers. In the current work, we study techniques to improve the robustness of our GAN-based approach by investigating whether a GAN can be trained to fool a selection of several neural network-based classifiers, or reduce the confidence of the classifications to a degree which makes them unreliable. Our results indicate that it is indeed possible to weaken a wider range of neural network classifiers by training the generator on several classifiers.

Bag of Features (BoF) has been successfully exploited in content-based image retrieval for several years. Due to its performance and popularity, several extensions have been proposed that involve feature description, dictionary building, feature encoding and post-query process, etc. This paper proposes a dynamically weighting scheme for BoF-based image retrieval based on feedback. It involves two contributions: (i) analyzing the statistical distribution characteristic of similar BoF representations and (ii) computing weights dynamically based on the feedback obtained from different initial query results. We quantitatively evaluate the proposed method on two different databases. Experiments confirm that the proposed weighting scheme has better performance than the baseline of BoF-based image retrieval systems. Meanwhile, the results demonstrate the effectiveness of the weighting scheme in terms of the precision of top-N returned images.

Autonomous mobile robotic teams are increasingly used in exploration of indoor environments. Accurate modeling of the world around the robot and describing the interaction of the robot with the world greatly increases the ability of the robot to act autonomously. This paper demonstrates the ability of autonomous robotic teams to find objects of interest. A novel feature of our approach is the object discovery and the use of it to augment the mapping and navigation process. The generated map can then be decomposed into semantic regions while also considering the distance and line of sight to anchor points. The advantage of this approach is that the robot can return a dense map of the region around an object of interest. The robustness of this approach is demonstrated in indoor environments with multiple platforms with the objective of discovering objects of interest.